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Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and Backtracking Search Algorithm

Author

Listed:
  • S. Hr. Aghay Kaboli

    (Sustainable Energy Research Center, Sultan Qaboos University, Muscat 123, Oman)

  • Amer Al Hinai

    (Sustainable Energy Research Center, Sultan Qaboos University, Muscat 123, Oman
    Department of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman)

  • A.H. Al-Badi

    (Department of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman)

  • Yassine Charabi

    (Center for Environmental Studies and Research, Sultan Qaboos University, Muscat 123, Oman)

  • Abdulrahim Al Saifi

    (Petroleum Development Oman (PDO), Muscat 100, Oman)

Abstract

The electromagnetic interference (EMI) generated by high voltage power systems can cause a serious problem for nearby electrically conductive structures, such as railroads, communication lines, or pipelines, that would place a system’s integrity and the operational safety of the structure at high level of risk. According to the IEEE standard-80, by implementing a well-designed mitigation system, the induced voltage on neighboring electrically conductive structure can reach a harmless level. The mitigation system can enhance the overall integrity of pipelines and provide higher operation safety for personal during working on the exposed parts of metallic pipelines or conductive appurtenances. An accurate prediction about the level of induced voltage is absolutely necessary to design a suitable mitigation system for metallic pipelines. Thus, in this work a hybrid prediction methodology composed of an adaptive neuro-fuzzy inference system (ANFIS) and a backtracking search algorithm (BSA) is developed to accurately predict the electromagnetic inference’s effects on metallic pipelines with shared right-of-way (RoW) and high voltage overhead lines (OHLs). Through the combination of BSA as a robust and efficient optimization algorithm in the learning process of an ANFIS approach, a hybrid data mining algorithm has been developed to predict the induced voltage on mitigated and unmitigated pipelines more accurately and reliably. The simulation results are validated by data sets observed from the Current Distribution, Electromagnetic Interference, Grounding and Soil Structure Analysis (CDEGS) software. From the simulation results it was confirmed that the proposed hybrid method is effective in accurately predicting the induced voltage on pipelines with changing system parameters. Furthermore, to evaluate the precision and applicability of the developed approach in this paper, its estimates are compared with the results obtained from an artificial neural network (ANN), a support vector regression (SVR) and an ANFIS optimized by other well-known optimization algorithms. The obtained results indicate higher accuracy of the developed hybrid method over other artificial intelligence based approaches.

Suggested Citation

  • S. Hr. Aghay Kaboli & Amer Al Hinai & A.H. Al-Badi & Yassine Charabi & Abdulrahim Al Saifi, 2019. "Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and Backtracking Search Algorithm," Energies, MDPI, vol. 12(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3651-:d:270317
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    References listed on IDEAS

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    1. AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
    2. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
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    Cited by:

    1. Arturo Popoli & Leonardo Sandrolini & Andrea Cristofolini, 2021. "Comparison of Screening Configurations for the Mitigation of Voltages and Currents Induced on Pipelines by HVAC Power Lines," Energies, MDPI, vol. 14(13), pages 1-18, June.
    2. Maher G. M. Abdolrasol & Mahammad Abdul Hannan & S. M. Suhail Hussain & Taha Selim Ustun & Mahidur R. Sarker & Pin Jern Ker, 2021. "Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks," Energies, MDPI, vol. 14(20), pages 1-19, October.
    3. Amer Al-Hinai & Yassine Charabi & Seyed H. Aghay Kaboli, 2021. "Offshore Wind Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    4. Stéfano Frizzo Stefenon & Roberto Zanetti Freire & Leandro dos Santos Coelho & Luiz Henrique Meyer & Rafael Bartnik Grebogi & William Gouvêa Buratto & Ademir Nied, 2020. "Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System," Energies, MDPI, vol. 13(2), pages 1-19, January.
    5. Hyoun-Su Kim & Hae-Yeol Min & J. Geoffrey Chase & Chul-Hwan Kim, 2021. "Analysis of Induced Voltage on Pipeline Located Close to Parallel Distribution System," Energies, MDPI, vol. 14(24), pages 1-13, December.

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